corrcounts_merge <- readRDS("~/VersionControl/senescence_benchmarking/Data/corrcounts_merge.rds")
metadata_merge <- readRDS("~/VersionControl/senescence_benchmarking/Data/metadata_merge.rds")
SenescenceSignatures <- readRDS("~/VersionControl/senescence_benchmarking/CommonFiles/SenescenceSignatures_divided_newCellAge.RDS")
library(markeR)
library(ggplot2)
library(ggpubr)
library(edgeR)
?markeR
?CalculateScores
ℹ Rendering development documentation for "CalculateScores"
df_ssGSEA <- CalculateScores(data = corrcounts_merge, metadata = metadata_merge, method = "ssGSEA", gene_sets = SenescenceSignatures)
Considering unidirectional gene signature mode for signature [DOWN]_CellAge
Warning: useNames = NA is deprecated. Instead, specify either useNames = TRUE or useNames = FALSE.
No id variables; using all as measure variables
Considering unidirectional gene signature mode for signature [DOWN]_HernandezSegura
Warning: useNames = NA is deprecated. Instead, specify either useNames = TRUE or useNames = FALSE.
No id variables; using all as measure variables
Considering unidirectional gene signature mode for signature [DOWN]_SeneQuest
Warning: useNames = NA is deprecated. Instead, specify either useNames = TRUE or useNames = FALSE.
No id variables; using all as measure variables
Considering unidirectional gene signature mode for signature [UP]_CellAge
Warning: useNames = NA is deprecated. Instead, specify either useNames = TRUE or useNames = FALSE.
No id variables; using all as measure variables
Considering unidirectional gene signature mode for signature [UP]_HernandezSegura
Warning: useNames = NA is deprecated. Instead, specify either useNames = TRUE or useNames = FALSE.
No id variables; using all as measure variables
Considering unidirectional gene signature mode for signature [UP]_SeneQuest
Warning: useNames = NA is deprecated. Instead, specify either useNames = TRUE or useNames = FALSE.
No id variables; using all as measure variables
Considering unidirectional gene signature mode for signature CSgene
Warning: useNames = NA is deprecated. Instead, specify either useNames = TRUE or useNames = FALSE.
No id variables; using all as measure variables
Considering unidirectional gene signature mode for signature GOBP_CELLULAR_SENESCENCE
Warning: useNames = NA is deprecated. Instead, specify either useNames = TRUE or useNames = FALSE.
No id variables; using all as measure variables
Considering unidirectional gene signature mode for signature GOBP_NEGATIVE_REGULATION_OF_CELLULAR_SENESCENCE
Warning: useNames = NA is deprecated. Instead, specify either useNames = TRUE or useNames = FALSE.
No id variables; using all as measure variables
Considering unidirectional gene signature mode for signature GOBP_POSITIVE_REGULATION_OF_CELLULAR_SENESCENCE
Warning: useNames = NA is deprecated. Instead, specify either useNames = TRUE or useNames = FALSE.
No id variables; using all as measure variables
Considering unidirectional gene signature mode for signature REACTOME_CELLULAR_SENESCENCE
Warning: useNames = NA is deprecated. Instead, specify either useNames = TRUE or useNames = FALSE.
No id variables; using all as measure variables
Considering unidirectional gene signature mode for signature SAUL_SEN_MAYO
Warning: useNames = NA is deprecated. Instead, specify either useNames = TRUE or useNames = FALSE.
No id variables; using all as measure variables
senescence_triggers_colors <- c(
"none" = "#E57373", # Soft red
"Radiation" = "#BDBDBD", # Medium gray
"DNA damage" = "#64B5F6", # Brighter blue
"Telomere shortening" = "#4FC3F7", # Vivid sky blue
"DNA demethylation" = "#BA68C8", # Rich lavender
"Oxidative stress" = "#FDD835", # Strong yellow
"Conditioned Medium" = "#F2994A", # Warm orange
"Oncogene" = "#81C784", # Medium green
"Lipid Accumulation" = "#E57373", # Coral
"Calcium influx" = "#26A69A", # Deep teal
"Plasma membrane dysruption" = "#D32F2F", # Strong salmon
"OSKM factors" = "#FFB74D", # Bright peach
"YAP KO" = "#9575CD" # Deep pastel purple
)
cellTypes_colors <- c(
"Fibroblast" = "#FF6961", # Strong Pastel Red
"Keratinocyte" = "#FFB347", # Strong Pastel Orange
"Melanocyte" = "#FFD700", # Strong Pastel Yellow
"Endothelial" = "#77DD77", # Strong Pastel Green
"Neuronal" = "#779ECB", # Strong Pastel Blue
"Mesenchymal" = "#C27BA0" # Strong Pastel Purple
)
cond_cohend <- list(A=c("Senescent"), # if no variable is defined, will be the first that appears in the ggplot
B=c("Proliferative","Quiescent"))
PlotScores(ResultsList = df_ssGSEA, ColorVariable = "CellType", GroupingVariable="Condition", method ="ssGSEA", ColorValues = cellTypes_colors, ConnectGroups=TRUE, ncol = 6, nrow = 2, widthTitle=20, y_limits = NULL, legend_nrow = 2,cond_cohend=cond_cohend)
df_logmedian <- CalculateScores(data = corrcounts_merge, metadata = metadata_merge, method = "logmedian", gene_sets = SenescenceSignatures)
senescence_triggers_colors <- c(
"none" = "#E57373", # Soft red
"Radiation" = "#BDBDBD", # Medium gray
"DNA damage" = "#64B5F6", # Brighter blue
"Telomere shortening" = "#4FC3F7", # Vivid sky blue
"DNA demethylation" = "#BA68C8", # Rich lavender
"Oxidative stress" = "#FDD835", # Strong yellow
"Conditioned Medium" = "#F2994A", # Warm orange
"Oncogene" = "#81C784", # Medium green
"Lipid Accumulation" = "#E57373", # Coral
"Calcium influx" = "#26A69A", # Deep teal
"Plasma membrane dysruption" = "#D32F2F", # Strong salmon
"OSKM factors" = "#FFB74D", # Bright peach
"YAP KO" = "#9575CD" # Deep pastel purple
)
cellTypes_colors <- c(
"Fibroblast" = "#FF6961", # Strong Pastel Red
"Keratinocyte" = "#FFB347", # Strong Pastel Orange
"Melanocyte" = "#FFD700", # Strong Pastel Yellow
"Endothelial" = "#77DD77", # Strong Pastel Green
"Neuronal" = "#779ECB", # Strong Pastel Blue
"Mesenchymal" = "#C27BA0" # Strong Pastel Purple
)
cond_cohend <- list(A=c("Senescent"), # if no variable is defined, will be the first that appears in the ggplot
B=c("Proliferative","Quiescent"))
PlotScores(ResultsList = df_logmedian, ColorVariable = "CellType", GroupingVariable="Condition", method ="logmedian", ColorValues = cellTypes_colors, ConnectGroups=TRUE, ncol = 6, nrow = 2, widthTitle=20, y_limits = NULL, legend_nrow = 2,xlab=NULL, cond_cohend = cond_cohend)
df_ranking <- CalculateScores(data = corrcounts_merge, metadata = metadata_merge, method = "ranking", gene_sets = SenescenceSignatures)
Considering unidirectional gene signature mode for signature [DOWN]_CellAge
Considering unidirectional gene signature mode for signature [DOWN]_HernandezSegura
Considering unidirectional gene signature mode for signature [DOWN]_SeneQuest
Considering unidirectional gene signature mode for signature [UP]_CellAge
Considering unidirectional gene signature mode for signature [UP]_HernandezSegura
Considering unidirectional gene signature mode for signature [UP]_SeneQuest
Considering unidirectional gene signature mode for signature CSgene
Considering unidirectional gene signature mode for signature GOBP_CELLULAR_SENESCENCE
Considering unidirectional gene signature mode for signature GOBP_NEGATIVE_REGULATION_OF_CELLULAR_SENESCENCE
Considering unidirectional gene signature mode for signature GOBP_POSITIVE_REGULATION_OF_CELLULAR_SENESCENCE
Considering unidirectional gene signature mode for signature REACTOME_CELLULAR_SENESCENCE
Considering unidirectional gene signature mode for signature SAUL_SEN_MAYO
senescence_triggers_colors <- c(
"none" = "#E57373", # Soft red
"Radiation" = "#BDBDBD", # Medium gray
"DNA damage" = "#64B5F6", # Brighter blue
"Telomere shortening" = "#4FC3F7", # Vivid sky blue
"DNA demethylation" = "#BA68C8", # Rich lavender
"Oxidative stress" = "#FDD835", # Strong yellow
"Conditioned Medium" = "#F2994A", # Warm orange
"Oncogene" = "#81C784", # Medium green
"Lipid Accumulation" = "#E57373", # Coral
"Calcium influx" = "#26A69A", # Deep teal
"Plasma membrane dysruption" = "#D32F2F", # Strong salmon
"OSKM factors" = "#FFB74D", # Bright peach
"YAP KO" = "#9575CD" # Deep pastel purple
)
cellTypes_colors <- c(
"Fibroblast" = "#FF6961", # Strong Pastel Red
"Keratinocyte" = "#FFB347", # Strong Pastel Orange
"Melanocyte" = "#FFD700", # Strong Pastel Yellow
"Endothelial" = "#77DD77", # Strong Pastel Green
"Neuronal" = "#779ECB", # Strong Pastel Blue
"Mesenchymal" = "#C27BA0" # Strong Pastel Purple
)
cond_cohend <- list(A=c("Senescent"), # if no variable is defined, will be the first that appears in the ggplot
B=c("Proliferative","Quiescent"))
PlotScores(ResultsList = df_ranking, ColorVariable = "CellType", GroupingVariable="Condition", method ="ranking", ColorValues = cellTypes_colors, ConnectGroups=TRUE, ncol = 6, nrow = 2, widthTitle=20, y_limits = NULL, legend_nrow = 2,xlab=NULL, cond_cohend = cond_cohend)
plotlist <- list()
for (sig in names(df_ssGSEA)){
df_subset_ssGSEA <- df_ssGSEA[[sig]]
df_subset_logmedian <- df_logmedian[[sig]]
df_subset_merge <- merge(df_subset_ssGSEA,df_subset_logmedian,by="sample")
# Wrap the signature name using the helper function
wrapped_title <- wrap_title_aux(sig, width = 20)
plotlist[[sig]] <- ggplot2::ggplot(df_subset_merge, aes(x=score.x, y=score.y)) +
geom_point(size=4, alpha=0.8, fill="darkgrey", shape=21) +
theme_bw() +
xlab("ssGSEA Enrichment Score") +
ylab("Normalised Signature Score") +
ggtitle(wrapped_title) +
theme(plot.title = ggplot2::element_text(hjust = 0.5, size=10),
plot.subtitle = ggplot2::element_text(hjust = 0.5))
}
ggpubr::ggarrange(plotlist=plotlist, nrow=3, ncol=4, align = "h")
Try scores with bidirectional signatures
bidirectsigs <- readRDS("~/VersionControl/senescence_benchmarking/CommonFiles/SenescenceSignatures_complete_newCellAge.RDS")
for (sig in names(bidirectsigs)){
sigdf <- bidirectsigs[[sig]]
sigdf <- sigdf[,1:2] # remove the third column, if applicable
if(any(sigdf[,2]=="not_reported")){
sigdf <- sigdf[,1]
bidirectsigs[[sig]] <- sigdf
next
}
sigdf[,2] <- ifelse(sigdf[,2]=="enriched",1,-1)
bidirectsigs[[sig]] <- sigdf
}
bidirectsigs
$CellAge
$CSgene
[1] "TP53" "TERF2" "MAPK14" "CDKN2A" "CDKN1A" "CCNE1" "CCNA1" "MAPKAPK5" "CBX4" "TXN" "TBX2"
[12] "STAT3" "SRF" "BMI1" "MAP2K4" "MAP2K6" "MAP2K3" "MAPK8" "MAPK3" "MAPK1" "PRKCD" "PML"
[23] "OPA1" "ATM" "MDM2" "CXCL8" "IL6" "IGFBP7" "ID1" "HRAS" "H2AFX" "POT1" "SIRT1"
[34] "KDM6B" "PLA2R1" "EZH2" "E2F3" "E2F1" "CEBPB" "CDKN2D" "CDKN2B" "CDKN1B" "CDK6" "CDK4"
[45] "CDK2" "CDC42" "RBX1" "CDC27" "CDK1" "MAML1" "CD44" "MAD2L1BP" "MAP4K4" "AIM2" "RECQL4"
[56] "ARHGAP18" "KL" "MAPKAPK2" "AURKB" "SLC16A7" "CCNE2" "HIST1H2BJ" "HIST1H3F" "CCNA2" "MCM3AP" "CDC16"
[67] "TSC22D1" "CBS" "TNFSF13" "CTNNAL1" "EED" "PNPT1" "CDC23" "RNASET2" "TP63" "CAV1" "MKNK1"
[78] "TSLP" "HIST1H2BK" "PPM1D" "HAVCR2" "CBX2" "KDM2B" "DPY30" "C2orf40" "YPEL3" "HIST2H4A" "HIST1H4L"
[89] "HIST1H4E" "HIST1H4B" "HIST1H4H" "HIST1H4C" "HIST1H4J" "HIST1H4K" "HIST1H4F" "HIST1H4D" "HIST1H4A" "HIST1H3B" "HIST1H3H"
[100] "HIST1H3J" "HIST1H3G" "HIST1H3I" "HIST1H3E" "HIST1H3C" "HIST1H3D" "HIST1H3A" "HIST2H2BE" "HIST1H2BO" "HIST1H2BC" "HIST1H2BI"
[111] "HIST1H2BH" "HIST1H2BE" "HIST1H2BF" "HIST1H2BM" "HIST1H2BN" "HIST1H2BL" "HIST1H2BG" "HIST2H2AC" "HIST2H2AA3" "HIST1H2AB" "HIST1H2AC"
[122] "HIST1H2AJ" "HIST1H4I" "HIST3H3" "CALR" "HMGA2" "PHC3" "KAT6A" "EHMT1" "SMC6" "AIMP2" "CALCA"
[133] "DEK" "MAPKAPK3" "ZNF148" "YY1" "WRN" "WNT5A" "NR1H2" "UBE3A" "UBE2E1" "UBE2D1" "UBC"
[144] "UBB" "UBA52" "CDC26P1" "TYMS" "TWIST1" "HIRA" "RPS27AP11" "HIST2H2AA4" "TP73" "TOPÂ 1,00" "TNF"
[155] "TGFB2" "TGFB1" "TFDP1" "TERT" "TERF1" "BUB1B" "BUB1" "TCF3" "TBX3" "TAGLN" "STAT6"
[166] "STAT1" "BRAF" "SREBF1" "BRCA1" "SP1" "SOX5" "SOD2" "SNAI1" "SMARCB1" "SMARCA2" "HIST2H3D"
[177] "PHC1P1" "ACD" "SKIL" "LOC649620" "SLC13A3" "LOC647654" "SMURF2" "ANAPC1" "SHC1" "CPEB1" "H3F3AP6"
[188] "ZMAT3" "RBBP4P1" "SRSF3" "SRSF1" "SATB1" "S100A6" "RXRB" "RRM2" "RRM1" "RPS27A" "RPS6KA3"
[199] "RPS6KA2" "RPS6KA1" "RPL5" "RNF2" "RIT1" "RING1" "BCL2L1" "RELA" "BCL2" "CCND1" "RBP2"
[210] "RBL2" "RBL1" "RBBP7" "RBBP4" "NTN4" "RB1" "IL21" "RAN" "RAF1" "RAC1" "TNRC6C"
[221] "KIAA1524" "EP400" "CNOT6" "CBX8" "PTEN" "SEPN1" "BACH1" "PSMB5" "PROX1" "PRL" "MAP2K7"
[232] "MAP2K1" "MAPK10" "MAPK9" "MAPK11" "MAPK7" "PRKDC" "RNF114" "PRKCI" "ATF7IP" "MFN1" "PRKAA2"
[243] "CDKN2AIP" "RBM38" "PRG2" "HIST2H4B" "HJURP" "TMEM140" "PBRM1" "Mar-05" "PPARG" "PPARD" "POU2F1"
[254] "TERF2IP" "ERRFI1" "H2BFS" "PLK1" "PLAUR" "PIN1" "PIM1" "PIK3CA" "PHB" "PGR" "PGD"
[265] "PIAS4" "PDGFB" "SIRT6" "ANAPC11" "ANAPC7" "ANAPC5" "WNT16" "FZR1" "ZBTB7A" "ERGIC2" "PCNA"
[276] "FIS1" "PAX3" "NOX4" "MINK1" "PEBP1" "YBX1" "NINJ1" "NFKB1" "H2AFB1" "NDN" "NCAM1"
[287] "NBN" "MYC" "MYBL2" "MSN" "ASS1" "LOC441488" "MRE11A" "MOV10" "MMP7" "MIF" "MAP3K5"
[298] "MAP3K1" "MECP2" "MCL1" "MAGEA2" "SMAD9" "SMAD7" "SMAD6" "SMAD5" "SMAD4" "SMAD3" "SMAD2"
[309] "SMAD1" "MAD2L1" "MXD1" "MIR34A" "MIR30A" "MIR299" "MIR29A" "MIR22" "MIR217" "MIR21" "MIR205"
[320] "MIR203A" "MIR191" "MIR146A" "MIR141" "MIR10B" "ARNTL" "LMNB1" "LMNA" "LGALS9" "RHOA" "KRT5"
[331] "KRAS" "KIT" "KIR2DL4" "KCNJ12" "JUN" "JAK2" "ITGB4" "IRS1" "IRF7" "IRF5" "IRF3"
[342] "ING1" "IDO1" "ILF3" "IL15" "IL12B" "CXCR2" "IL4" "IGFBP5" "IGFBP3" "IGFBP1" "IGF1R"
[353] "IGF1" "H3F3AP5" "IFNG" "IFI16" "IDH1" "ID2" "HIST2H3A" "BIRC5" "HSPB1" "HSPA9" "HSPA5"
[364] "HSPA1A" "APEX1" "HNRNPA1" "FOXA3" "FOXA2" "FOXA1" "HMGA1" "HIF1A" "ANXA5" "HELLS" "HDAC1"
[375] "H3F3B" "H3F3A" "HIST1H2BB" "HIST1H2BD" "H2AFZ" "HIST1H2AD" "HIST1H2AE" "ANAPC4" "ANAPC2" "UBN1" "SENP1"
[386] "GUCY2C" "GSK3B" "UHRF1" "BRD7" "NSMCE2" "PTRF" "GPI" "GNAO1" "RPS6KA6" "TNRC6A" "AGO2"
[397] "B3GAT1" "DNAJC2" "GJA1" "AGO1" "EHF" "TINF2" "LDLRAP1" "ULK3" "GAPDH" "ABI3BP" "ASF1A"
[408] "HIST1H2BA" "G6PD" "ACKR1" "MTOR" "CDC26" "CNOT6L" "FOS" "CABIN1" "MORC3" "SUZ12" "NPTXR"
[419] "CBX6" "SIRT3" "CRTC1" "PPP1R13B" "SUN1" "SMC5" "TNRC6B" "FOXO1" "FOXM1" "TNIK" "SCMH1"
[430] "DKKÂ 1,00" "FGFR2" "FGF2" "HEPACAM" "FANCD2" "EWSR1" "ETS2" "ETS1" "ESR2" "ERF" "AKT1"
[441] "EREG" "ERBB2" "ENG" "ELN" "CRTC2" "EIF5A" "EGR1" "EGFR" "EEF1B2" "AGO4" "AGO3"
[452] "EEF1A1" "PHC2" "PHC1" "ABCA1" "E2F2" "DUSP6" "DUSP4" "HBEGF" "AGT" "DNMT3A" "AGER"
[463] "DKC1" "DAXX" "CYP3A4" "CTSZ" "CTSD" "CSNK2A1" "E2F7" "PARP1" "HIST3H2BB" "HIST2H3C" "JDP2"
[474] "HIST4H4" "CLU" "CKB" "RASSF1" "CHEK1" "TOPBP1" "UBE2C" "KIF2C" "BTG3" "EHMT2" "GADD45G"
[485] "NEK6" "ZMYND11" "SPINT2" "CENPA" "AGR2" "CEBPG" "HYOU1" "TADA3" "MCRS1" "NDRG1" "ANAPC10"
[496] "CDKN2C" "ZMPSTE24" "PSMD14" "NAMPT" "RAD50" "TRIM10" "DNM1L" "BCL2L11"
$GOBP_CELLULAR_SENESCENCE
[1] "AKT3" "MIR543" "CDK2" "CDK6" "CDKN1A" "ZMPSTE24" "CDKN1B" "CDKN2A" "CDKN2B" "CITED2" "KAT5" "PLK2" "NEK6"
[14] "ZNF277" "CGAS" "COMP" "MAPK14" "VASH1" "PLA2R1" "SMC5" "SIRT1" "MORC3" "NUP62" "ABL1" "ULK3" "RSL1D1"
[27] "FBXO5" "FBXO4" "MAGEA2B" "NSMCE2" "H2AX" "HLA-G" "HMGA1" "HRAS" "ID2" "IGF1R" "ING2" "KIR2DL4" "ARG2"
[40] "LMNA" "BMAL1" "MIR10A" "MIR146A" "MIR17" "MIR188" "MIR217" "MIR22" "MIR34A" "MAGEA2" "MAP3K3" "MAP3K5" "MIF"
[53] "MNT" "ATM" "NPM1" "YBX1" "OPA1" "PAWR" "ABI3" "FZR1" "WNT16" "SIRT6" "PML" "PRMT6" "PRELP"
[66] "PRKCD" "MAPK8" "MAPK11" "MAPK9" "MAPK10" "MAP2K1" "MAP2K3" "MAP2K6" "MAP2K7" "B2M" "ZMIZ1" "PTEN" "MIR20B"
[79] "RBL1" "BCL6" "MAP2K4" "BMPR1A" "SPI1" "SRF" "BRCA2" "NEK4" "TBX2" "TBX3" "MIR590" "TERC" "TERF2"
[92] "TERT" "TOP2B" "TP53" "TWIST1" "WNT1" "WRN" "SMC6" "KAT6A" "ZKSCAN3" "HMGA2" "CALR" "YPEL3" "ECRG4"
[105] "MAPKAPK5" "TP63" "PNPT1" "DNAJA3" "EEF1E1" "NUAK1"
$GOBP_NEGATIVE_REGULATION_OF_CELLULAR_SENESCENCE
$GOBP_POSITIVE_REGULATION_OF_CELLULAR_SENESCENCE
$HernandezSegura
$REACTOME_CELLULAR_SENESCENCE
[1] "CDC27" "E2F2" "SCMH1" "MRE11" "MAP2K3" "MAPK9" "ANAPC4" "MAP2K4" "MAP4K4" "RPS6KA2" "UBE2D1" "EED" "MAP2K7"
[14] "TNRC6C" "MAPKAPK5" "ANAPC5" "TNRC6A" "TINF2" "AGO1" "CDC23" "CABIN1" "MAPK1" "HIRA" "TNRC6B" "E2F1" "RBBP7"
[27] "MAPK3" "ACD" "NBN" "CCNE1" "FZR1" "ERF" "CDK6" "H2AZ2" "EZH2" "MAPK8" "UBE2S" "MAP2K6" "NFKB1"
[40] "MAPK10" "ANAPC15" "CDKN1B" "PHC1" "ASF1A" "MAPK14" "E2F3" "LMNB1" "RAD50" "TFDP2" "MAPKAPK3" "IL1A" "RPS6KA1"
[53] "UBN1" "RNF2" "CDKN2C" "CDK2" "H1-3" "H1-1" "H2BC11" "CDKN1A" "ID1" "AGO3" "POT1" "CDKN2D" "CDC16"
[66] "H3-3B" "KDM6B" "TERF2" "CCNA1" "PHC2" "AGO4" "ETS1" "CDK4" "MDM2" "IL6" "TXN" "HMGA1" "RB1"
[79] "MINK1" "TP53" "ANAPC11" "CBX8" "CBX4" "RPS27A" "CCNA2" "H2BC1" "TERF1" "CDKN2B" "CDKN2A" "ATM" "HMGA2"
[92] "UBC" "VENTX" "ANAPC1" "TNIK" "MOV10" "ETS2" "H2BC5" "H4C8" "RBBP4" "MAPKAPK2" "H3-3A" "IGFBP7" "ANAPC10"
[105] "ANAPC16" "MAPK7" "TERF2IP" "H3-4" "BMI1" "H1-4" "STAT3" "CXCL8" "UBE2E1" "UBB" "FOS" "IFNB1" "CEBPB"
[118] "KAT5" "RELA" "PHC3" "CBX2" "UBE2C" "CCNE2" "ANAPC2" "CDC26" "RPS6KA3" "JUN" "SUZ12" "H2AC6" "H2BC4"
[131] "EHMT1" "EP400" "H3C13" "CBX6" "H2AC20" "H1-5" "H2BC21" "H2BC13" "MAPK11" "SP1" "H1-2" "H2AX" "H1-0"
[144] "ANAPC7" "H2AC7" "H2BC26" "H4C3" "H3C12" "H4C11" "H3C4" "MAP3K5" "H4C16" "H2BC12" "TFDP1" "MDM4" "H3C14"
[157] "H3C15" "RING1" "EHMT2" "UBA52" "H2AJ" "H4C15" "H4C14" "H4C12" "H2BC14" "H2BC8" "H3C8" "H2AB1" "H2BC6"
[170] "H4C6" "H2BC17" "H3C6" "H4C13" "H3C11" "H2BC9" "H3C1" "H4C9" "H2AC14" "H2BC3" "H4C5" "H2AC8" "H4C4"
[183] "H2BC7" "H3C7" "H2AC4" "H2BC10" "H4C1" "H4C2" "H3C10" "MIR24-2" "MIR24-1" "H3C2" "H3C3" "H2AC18" "H2AC19"
$SAUL_SEN_MAYO
$SeneQuest
NA
df_logmedian <- CalculateScores(data = corrcounts_merge, metadata = metadata_merge, method = "logmedian", gene_sets = bidirectsigs)
Considering bidirectional gene signature mode for signature CellAge
Considering unidirectional gene signature mode for signature CSgene
Considering unidirectional gene signature mode for signature GOBP_CELLULAR_SENESCENCE
Considering unidirectional gene signature mode for signature GOBP_NEGATIVE_REGULATION_OF_CELLULAR_SENESCENCE
Considering unidirectional gene signature mode for signature GOBP_POSITIVE_REGULATION_OF_CELLULAR_SENESCENCE
Considering bidirectional gene signature mode for signature HernandezSegura
Considering unidirectional gene signature mode for signature REACTOME_CELLULAR_SENESCENCE
Considering unidirectional gene signature mode for signature SAUL_SEN_MAYO
Considering bidirectional gene signature mode for signature SeneQuest
senescence_triggers_colors <- c(
"none" = "#E57373", # Soft red
"Radiation" = "#BDBDBD", # Medium gray
"DNA damage" = "#64B5F6", # Brighter blue
"Telomere shortening" = "#4FC3F7", # Vivid sky blue
"DNA demethylation" = "#BA68C8", # Rich lavender
"Oxidative stress" = "#FDD835", # Strong yellow
"Conditioned Medium" = "#F2994A", # Warm orange
"Oncogene" = "#81C784", # Medium green
"Lipid Accumulation" = "#E57373", # Coral
"Calcium influx" = "#26A69A", # Deep teal
"Plasma membrane dysruption" = "#D32F2F", # Strong salmon
"OSKM factors" = "#FFB74D", # Bright peach
"YAP KO" = "#9575CD" # Deep pastel purple
)
cellTypes_colors <- c(
"Fibroblast" = "#FF6961", # Strong Pastel Red
"Keratinocyte" = "#FFB347", # Strong Pastel Orange
"Melanocyte" = "#FFD700", # Strong Pastel Yellow
"Endothelial" = "#77DD77", # Strong Pastel Green
"Neuronal" = "#779ECB", # Strong Pastel Blue
"Mesenchymal" = "#C27BA0" # Strong Pastel Purple
)
cond_cohend <- list(A=c("Senescent"), # if no variable is defined, will be the first that appears in the ggplot
B=c("Proliferative","Quiescent"))
PlotScores(ResultsList = df_logmedian, ColorVariable = "CellType", GroupingVariable="Condition", method ="logmedian", ColorValues = cellTypes_colors, ConnectGroups=TRUE, ncol = 3, nrow = 3, widthTitle=20, y_limits = NULL, legend_nrow = 2,xlab=NULL, cond_cohend = cond_cohend)
df_ssgsea <- CalculateScores(data = corrcounts_merge, metadata = metadata_merge, method = "ssGSEA", gene_sets = bidirectsigs)
Considering bidirectional gene signature mode for signature CellAge
Warning: useNames = NA is deprecated. Instead, specify either useNames = TRUE or useNames = FALSE.
No id variables; using all as measure variables
Warning: useNames = NA is deprecated. Instead, specify either useNames = TRUE or useNames = FALSE.
No id variables; using all as measure variables
Considering unidirectional gene signature mode for signature CSgene
Warning: useNames = NA is deprecated. Instead, specify either useNames = TRUE or useNames = FALSE.
No id variables; using all as measure variables
Considering unidirectional gene signature mode for signature GOBP_CELLULAR_SENESCENCE
Warning: useNames = NA is deprecated. Instead, specify either useNames = TRUE or useNames = FALSE.
No id variables; using all as measure variables
Considering unidirectional gene signature mode for signature GOBP_NEGATIVE_REGULATION_OF_CELLULAR_SENESCENCE
Warning: useNames = NA is deprecated. Instead, specify either useNames = TRUE or useNames = FALSE.
No id variables; using all as measure variables
Considering unidirectional gene signature mode for signature GOBP_POSITIVE_REGULATION_OF_CELLULAR_SENESCENCE
Warning: useNames = NA is deprecated. Instead, specify either useNames = TRUE or useNames = FALSE.
No id variables; using all as measure variables
Considering bidirectional gene signature mode for signature HernandezSegura
Warning: useNames = NA is deprecated. Instead, specify either useNames = TRUE or useNames = FALSE.
No id variables; using all as measure variables
Warning: useNames = NA is deprecated. Instead, specify either useNames = TRUE or useNames = FALSE.
No id variables; using all as measure variables
Considering unidirectional gene signature mode for signature REACTOME_CELLULAR_SENESCENCE
Warning: useNames = NA is deprecated. Instead, specify either useNames = TRUE or useNames = FALSE.
No id variables; using all as measure variables
Considering unidirectional gene signature mode for signature SAUL_SEN_MAYO
Warning: useNames = NA is deprecated. Instead, specify either useNames = TRUE or useNames = FALSE.
No id variables; using all as measure variables
Considering bidirectional gene signature mode for signature SeneQuest
Warning: useNames = NA is deprecated. Instead, specify either useNames = TRUE or useNames = FALSE.
No id variables; using all as measure variables
Warning: useNames = NA is deprecated. Instead, specify either useNames = TRUE or useNames = FALSE.
No id variables; using all as measure variables
senescence_triggers_colors <- c(
"none" = "#E57373", # Soft red
"Radiation" = "#BDBDBD", # Medium gray
"DNA damage" = "#64B5F6", # Brighter blue
"Telomere shortening" = "#4FC3F7", # Vivid sky blue
"DNA demethylation" = "#BA68C8", # Rich lavender
"Oxidative stress" = "#FDD835", # Strong yellow
"Conditioned Medium" = "#F2994A", # Warm orange
"Oncogene" = "#81C784", # Medium green
"Lipid Accumulation" = "#E57373", # Coral
"Calcium influx" = "#26A69A", # Deep teal
"Plasma membrane dysruption" = "#D32F2F", # Strong salmon
"OSKM factors" = "#FFB74D", # Bright peach
"YAP KO" = "#9575CD" # Deep pastel purple
)
cellTypes_colors <- c(
"Fibroblast" = "#FF6961", # Strong Pastel Red
"Keratinocyte" = "#FFB347", # Strong Pastel Orange
"Melanocyte" = "#FFD700", # Strong Pastel Yellow
"Endothelial" = "#77DD77", # Strong Pastel Green
"Neuronal" = "#779ECB", # Strong Pastel Blue
"Mesenchymal" = "#C27BA0" # Strong Pastel Purple
)
cond_cohend <- list(A=c("Senescent"), # if no variable is defined, will be the first that appears in the ggplot
B=c("Proliferative","Quiescent"))
PlotScores(ResultsList = df_ssgsea, ColorVariable = "CellType", GroupingVariable="Condition", method ="ssGSEA", ColorValues = cellTypes_colors, ConnectGroups=TRUE, ncol = 3, nrow = 3, widthTitle=20, y_limits = NULL, legend_nrow = 2,xlab=NULL, cond_cohend = cond_cohend)
df_ranking <- CalculateScores(data = corrcounts_merge, metadata = metadata_merge, method = "ranking", gene_sets = bidirectsigs)
Considering bidirectional gene signature mode for signature CellAge
Considering unidirectional gene signature mode for signature CSgene
Considering unidirectional gene signature mode for signature GOBP_CELLULAR_SENESCENCE
Considering unidirectional gene signature mode for signature GOBP_NEGATIVE_REGULATION_OF_CELLULAR_SENESCENCE
Considering unidirectional gene signature mode for signature GOBP_POSITIVE_REGULATION_OF_CELLULAR_SENESCENCE
Considering bidirectional gene signature mode for signature HernandezSegura
Considering unidirectional gene signature mode for signature REACTOME_CELLULAR_SENESCENCE
Considering unidirectional gene signature mode for signature SAUL_SEN_MAYO
Considering bidirectional gene signature mode for signature SeneQuest
senescence_triggers_colors <- c(
"none" = "#E57373", # Soft red
"Radiation" = "#BDBDBD", # Medium gray
"DNA damage" = "#64B5F6", # Brighter blue
"Telomere shortening" = "#4FC3F7", # Vivid sky blue
"DNA demethylation" = "#BA68C8", # Rich lavender
"Oxidative stress" = "#FDD835", # Strong yellow
"Conditioned Medium" = "#F2994A", # Warm orange
"Oncogene" = "#81C784", # Medium green
"Lipid Accumulation" = "#E57373", # Coral
"Calcium influx" = "#26A69A", # Deep teal
"Plasma membrane dysruption" = "#D32F2F", # Strong salmon
"OSKM factors" = "#FFB74D", # Bright peach
"YAP KO" = "#9575CD" # Deep pastel purple
)
cellTypes_colors <- c(
"Fibroblast" = "#FF6961", # Strong Pastel Red
"Keratinocyte" = "#FFB347", # Strong Pastel Orange
"Melanocyte" = "#FFD700", # Strong Pastel Yellow
"Endothelial" = "#77DD77", # Strong Pastel Green
"Neuronal" = "#779ECB", # Strong Pastel Blue
"Mesenchymal" = "#C27BA0" # Strong Pastel Purple
)
cond_cohend <- list(A=c("Senescent"), # if no variable is defined, will be the first that appears in the ggplot
B=c("Proliferative","Quiescent"))
PlotScores(ResultsList = df_ranking, ColorVariable = "CellType", GroupingVariable="Condition", method ="ranking", ColorValues = cellTypes_colors, ConnectGroups=TRUE, ncol = 3, nrow = 3, widthTitle=20, y_limits = NULL, legend_nrow = 2,xlab=NULL, cond_cohend = cond_cohend)
PlotScores(data = corrcounts_merge,
metadata = metadata_merge,
gene_sets=bidirectsigs,
GroupingVariable="Condition",
method ="all",
ncol = NULL,
nrow = NULL,
widthTitle=30,
limits = NULL,
title="Marthandan et al. 2016",
titlesize = 12,
ColorValues = NULL)
Considering bidirectional gene signature mode for signature CellAge
Warning: useNames = NA is deprecated. Instead, specify either useNames = TRUE or useNames = FALSE.
No id variables; using all as measure variables
Warning: useNames = NA is deprecated. Instead, specify either useNames = TRUE or useNames = FALSE.
No id variables; using all as measure variables
Considering unidirectional gene signature mode for signature CSgene
Warning: useNames = NA is deprecated. Instead, specify either useNames = TRUE or useNames = FALSE.
No id variables; using all as measure variables
Considering unidirectional gene signature mode for signature GOBP_CELLULAR_SENESCENCE
Warning: useNames = NA is deprecated. Instead, specify either useNames = TRUE or useNames = FALSE.
No id variables; using all as measure variables
Considering unidirectional gene signature mode for signature GOBP_NEGATIVE_REGULATION_OF_CELLULAR_SENESCENCE
Warning: useNames = NA is deprecated. Instead, specify either useNames = TRUE or useNames = FALSE.
No id variables; using all as measure variables
Considering unidirectional gene signature mode for signature GOBP_POSITIVE_REGULATION_OF_CELLULAR_SENESCENCE
Warning: useNames = NA is deprecated. Instead, specify either useNames = TRUE or useNames = FALSE.
No id variables; using all as measure variables
Considering bidirectional gene signature mode for signature HernandezSegura
Warning: useNames = NA is deprecated. Instead, specify either useNames = TRUE or useNames = FALSE.
No id variables; using all as measure variables
Warning: useNames = NA is deprecated. Instead, specify either useNames = TRUE or useNames = FALSE.
No id variables; using all as measure variables
Considering unidirectional gene signature mode for signature REACTOME_CELLULAR_SENESCENCE
Warning: useNames = NA is deprecated. Instead, specify either useNames = TRUE or useNames = FALSE.
No id variables; using all as measure variables
Considering unidirectional gene signature mode for signature SAUL_SEN_MAYO
Warning: useNames = NA is deprecated. Instead, specify either useNames = TRUE or useNames = FALSE.
No id variables; using all as measure variables
Considering bidirectional gene signature mode for signature SeneQuest
Warning: useNames = NA is deprecated. Instead, specify either useNames = TRUE or useNames = FALSE.
No id variables; using all as measure variables
Warning: useNames = NA is deprecated. Instead, specify either useNames = TRUE or useNames = FALSE.
No id variables; using all as measure variables
Considering bidirectional gene signature mode for signature CellAge
Considering unidirectional gene signature mode for signature CSgene
Considering unidirectional gene signature mode for signature GOBP_CELLULAR_SENESCENCE
Considering unidirectional gene signature mode for signature GOBP_NEGATIVE_REGULATION_OF_CELLULAR_SENESCENCE
Considering unidirectional gene signature mode for signature GOBP_POSITIVE_REGULATION_OF_CELLULAR_SENESCENCE
Considering bidirectional gene signature mode for signature HernandezSegura
Considering unidirectional gene signature mode for signature REACTOME_CELLULAR_SENESCENCE
Considering unidirectional gene signature mode for signature SAUL_SEN_MAYO
Considering bidirectional gene signature mode for signature SeneQuest
Considering bidirectional gene signature mode for signature CellAge
Considering unidirectional gene signature mode for signature CSgene
Considering unidirectional gene signature mode for signature GOBP_CELLULAR_SENESCENCE
Considering unidirectional gene signature mode for signature GOBP_NEGATIVE_REGULATION_OF_CELLULAR_SENESCENCE
Considering unidirectional gene signature mode for signature GOBP_POSITIVE_REGULATION_OF_CELLULAR_SENESCENCE
Considering bidirectional gene signature mode for signature HernandezSegura
Considering unidirectional gene signature mode for signature REACTOME_CELLULAR_SENESCENCE
Considering unidirectional gene signature mode for signature SAUL_SEN_MAYO
Considering bidirectional gene signature mode for signature SeneQuest
# missing:
# - combine legends
# - wrap title
# - tilt x labels to 60 degrees
# - change default colors
# wrap x labels with wrap_title
# grid with common legends https://support.bioconductor.org/p/87318/
IndividualGenes_Violins(data = corrcounts_merge, metadata = metadata_merge, genes = c("CDKN1A", "CDKN2A", "GLB1","TP53","CCL2"), GroupingVariable = "Condition", plot=T, ncol=NULL, nrow=2, divide="CellType", invert_divide=FALSE,ColorValues=senescence_triggers_colors, pointSize=2, ColorVariable="SenescentType", title="Senescence", widthTitle=16,y_limits = NULL,legend_nrow=4, xlab="Condition",colorlab="")
Using gene as id variables
CorrelationHeatmap(data=corrcounts_merge,
metadata = metadata_merge,
genes=c("CDKN1A", "CDKN2A", "GLB1","TP53","CCL2"),
separate.by = "Condition",
method = "pearson",
colorlist = list(low = "#3F4193", mid = "#F9F4AE", high = "#B44141"),
limits_colorscale = c(-1,0,1),
widthTitle = 16,
title = "test",
cluster_rows = TRUE,
cluster_columns = TRUE,
detailedresults = FALSE,
legend_position="right",
titlesize=20)
Warning: Heatmap/annotation names are duplicated: pearson's coefficient
Warning: Heatmap/annotation names are duplicated: pearson's coefficient, pearson's coefficient
Warning: `legend_height` you specified is too small, use the default minimal height.
Warning: `legend_height` you specified is too small, use the default minimal height.
Warning: `legend_height` you specified is too small, use the default minimal height.
degenes <- calculateDE(data=corrcounts_merge,
metadata=metadata_merge,
variables="Condition",
lmexpression = NULL,
modelmat = NULL,
contrasts = c("Senescent - Proliferative",
"Senescent - Quiescent",
"Proliferative - Quiescent"))
degenes
$`Senescent - Proliferative`
$`Senescent - Quiescent`
$`Proliferative - Quiescent`
NA
GSEAresults <- runGSEA(degenes, bidirectsigs, stat = NULL)
Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, :
There are ties in the preranked stats (0.03% of the list).
The order of those tied genes will be arbitrary, which may produce unexpected results.
Warning in fgseaMultilevel(pathways = pathways, stats = stats, minSize = minSize, :
For some of the pathways the P-values were likely overestimated. For such pathways log2err is set to NA.
Warning in fgseaMultilevel(pathways = pathways, stats = stats, minSize = minSize, :
For some pathways, in reality P-values are less than 1e-50. You can set the `eps` argument to zero for better estimation.
Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, :
There are ties in the preranked stats (0.03% of the list).
The order of those tied genes will be arbitrary, which may produce unexpected results.
Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, :
There are ties in the preranked stats (0.03% of the list).
The order of those tied genes will be arbitrary, which may produce unexpected results.
Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, :
There are ties in the preranked stats (0.03% of the list).
The order of those tied genes will be arbitrary, which may produce unexpected results.
Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, :
There are ties in the preranked stats (0.03% of the list).
The order of those tied genes will be arbitrary, which may produce unexpected results.
Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, :
There are ties in the preranked stats (0.03% of the list).
The order of those tied genes will be arbitrary, which may produce unexpected results.
Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, :
There are ties in the preranked stats (0.03% of the list).
The order of those tied genes will be arbitrary, which may produce unexpected results.
Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, :
There are ties in the preranked stats (0.03% of the list).
The order of those tied genes will be arbitrary, which may produce unexpected results.
Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, :
There are ties in the preranked stats (0.03% of the list).
The order of those tied genes will be arbitrary, which may produce unexpected results.
Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, :
There are ties in the preranked stats (0.03% of the list).
The order of those tied genes will be arbitrary, which may produce unexpected results.
Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, :
There are ties in the preranked stats (0.03% of the list).
The order of those tied genes will be arbitrary, which may produce unexpected results.
Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, :
There are ties in the preranked stats (0.03% of the list).
The order of those tied genes will be arbitrary, which may produce unexpected results.
Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, :
There are ties in the preranked stats (0.03% of the list).
The order of those tied genes will be arbitrary, which may produce unexpected results.
Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, :
There are ties in the preranked stats (0.03% of the list).
The order of those tied genes will be arbitrary, which may produce unexpected results.
Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, :
There are ties in the preranked stats (0.03% of the list).
The order of those tied genes will be arbitrary, which may produce unexpected results.
Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, :
There are ties in the preranked stats (0.03% of the list).
The order of those tied genes will be arbitrary, which may produce unexpected results.
Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, :
There are ties in the preranked stats (0.03% of the list).
The order of those tied genes will be arbitrary, which may produce unexpected results.
Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, :
There are ties in the preranked stats (0.03% of the list).
The order of those tied genes will be arbitrary, which may produce unexpected results.
Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, :
There are ties in the preranked stats (0.03% of the list).
The order of those tied genes will be arbitrary, which may produce unexpected results.
Warning in fgseaMultilevel(pathways = pathways, stats = stats, minSize = minSize, :
For some of the pathways the P-values were likely overestimated. For such pathways log2err is set to NA.
Warning in fgseaMultilevel(pathways = pathways, stats = stats, minSize = minSize, :
For some pathways, in reality P-values are less than 1e-50. You can set the `eps` argument to zero for better estimation.
Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, :
There are ties in the preranked stats (0.03% of the list).
The order of those tied genes will be arbitrary, which may produce unexpected results.
Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, :
There are ties in the preranked stats (0.03% of the list).
The order of those tied genes will be arbitrary, which may produce unexpected results.
Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, :
There are ties in the preranked stats (0.03% of the list).
The order of those tied genes will be arbitrary, which may produce unexpected results.
Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, :
There are ties in the preranked stats (0.03% of the list).
The order of those tied genes will be arbitrary, which may produce unexpected results.
Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, :
There are ties in the preranked stats (0.03% of the list).
The order of those tied genes will be arbitrary, which may produce unexpected results.
Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, :
There are ties in the preranked stats (0.03% of the list).
The order of those tied genes will be arbitrary, which may produce unexpected results.
Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, :
There are ties in the preranked stats (0.03% of the list).
The order of those tied genes will be arbitrary, which may produce unexpected results.
Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, :
There are ties in the preranked stats (0.03% of the list).
The order of those tied genes will be arbitrary, which may produce unexpected results.
GSEAresults
$`Senescent - Proliferative`
$`Senescent - Quiescent`
$`Proliferative - Quiescent`
NA
plotNESlollipop(GSEA_results=GSEAresults, sig_threshold = 0.05,
low_color = "blue", mid_color = "white", high_color = "red",
grid = T, nrow = 1, ncol = NULL, padj_limit=c(0,0.1), widthlabels=28, title=NULL)
NA